Through controlled ablations on an NVIDIA H100 it is shown that decode throughput is governed not by occupancy, compute, address scatter, or launch parallelism, but by work granularity: throughput is a function of the average match length, because a short match leaves most lanes of a cooperating warp idle.
Abstract
The ACEAPEX line of work established a lossless LZ77 format whose back-references are absolute output positions, giving parallel, compressed-resident GPU decode with sub-millisecond region seek. What it did not establish is what governs the decode throughput of such a format, or how to improve it. This paper answers both. Through controlled ablations on an NVIDIA H100 we show that decode throughput is governed not by occupancy, compute, address scatter, or launch parallelism, but by work granularity: throughput is a function of the average match length, because a short match leaves most lanes of a cooperating warp idle. A synthetic copy kernel confirms a 3.5x throughput span (212 to 744 GB/s) as average match length grows from 32 to 1024 bytes. Real data sit at the low end (mean match length 6.5 on enwik9, 10.1 on FASTQ). We then show that this mechanism yields a practical, encode-side lever: raising the minimum match length by distance class (6/8/10/12 to 12/16/24/32) improves both compression ratio and decode throughput simultaneously on all eight tested datasets, with no exceptions and no change to the decode kernel. FASTQ decode rises from 142.6 to 178.6 GB/s while ratio improves 1.8%; enwik9 throughput rises 78%. This is not a trade-off: both gains follow from one cause, removing short matches whose far offsets cost more entropy than they save. All figures are bit-perfect (FNV on GPU paths, byte compare on CPU paths) and git-verifiable. Scope is explicit: figures are match-phase, device-resident; entropy and host transfer are outside the timer; seek is read/block-level, not coordinate-level; and we do not claim to exceed the hardware bandwidth ceiling.
Across three decoder architectures on an H100 the authors measure that parse, not copy, holds 64-72% of device-resident decode time; that bounding back-reference chain depth - provable, and costing 0.006% in ratio - moves latency by at most 2.8% and, for the file's own latency spike, provably by nothing at all.
Existing fast GPU error-bounded lossy compressors have achieved high throughput through pure-GPU single-kernel designs, but their compression ratios remain limited because they typically apply a fixed first-order predictor on independent blocks. We propose FSZ, a GPU error-bounded lossy compressor that redesigns the prediction stage with three mutually reinforcing algorithmic innovations to achieve both higher compression ratios and higher throughput within a single CUDA kernel: (1) cross-block prediction state carries Lorenzo prediction state across block boundaries within 256-element tiles, eliminating 7 out of 8 boundary residuals that inflate encoding rates; (2) per-tile adaptive multi-order prediction and centering adaptively selects the best compression strategy per tile from first-order, second-order, and centering variants; and (3) a single-pass four-way evaluation exploits a mathematical property of finite differences to evaluate all variants from a single data read, enabling richer prediction within the same bandwidth budget as a fixed predictor. Experiments on NVIDIA GH200 GPU with 8 real-world application datasets show that FSZ outperforms cuSZp-P by up to 10.95x and the state-of-the-art cuSZp-O by up to 2.92x in compression ratio. Notably, these gains come with no throughput penalty: FSZ simultaneously achieves the highest average throughput (676 GB/s compression, 785 GB/s decompression) among all evaluated compressors.
Evo 2 is the largest openly available genomic foundation model, but its forty billion parameter configuration cannot be loaded onto a single 80 GB accelerator, placing genome-scale analysis beyond most laboratories. We present TurboQuant-Bio, an open toolkit that compresses Evo 2’s weights and attention cache to four bits without calibration data, and serves both through fused kernels. Compression is near-lossless across perplexity spanning the tree of life, genomic classification, splice-site prediction, gene completion and clinically relevant variant-effect prediction. It brings Evo 2 40B onto one 80 GB GPU and Evo 2 7B to its full million-token context within a 40 GB memory budget, an eightfold gain in reachable context. We further show that the released chunked-prefill path is silently incorrect, returning plausible but uncorrelated likelihoods, and derive the block-wise continuation that repairs it: a complete 580-kilobase bacterial genome is now scored in one context in 22 minutes rather than 13.7 hours.
Michail Patsakis, Alexandros Tzanakakis, I. Georgakopoulos-Soares· bioRxiv· 0 citations
Graphics processing units (GPUs) underpin high-performance computing, but device partitioning does not ensure that an in-context pointer remains within its allocation. We present a hardware–software co-design whose 16-bit tag uses odd parity and fail-safe class encoding. It combines a variable-precision extent, an aligned CRC checker, and an exact-bounds micro-cache. For 200,000 log-uniform requests from 16 B to 1 TiB, mean Class 4 fragmentation is 1.638%, versus 27.733% for power-of-two encoding. The seven-bit aligned signature has full GF(2) rank and a 1/128 non-adaptive collision rate; its deterministic window is exactly one through three regions and tight at four. Exhaustive testing rejects every one-bit tag corruption, while two-bit analysis demonstrates why parity is not adversarial authentication. A SAT-equivalent endpoint rewrite reduces Class 3 generic depth from 60 to 24 levels. A fail-closed 32-lane Class 4 topology detects nonuniform active-lane tags in hardware; for uniform tags, it reduces generic CMOS cost from 155,200 to 72,872 transistor equivalents (53.05%) and depth from 67 to 61 levels. Official SASS traces provide an analytical exposure bound rather than native simulation; a separate pre-layout 45 nm mapping is reported only as a timing sensitivity experiment.
Dan Toderici, T. Enache, R. Rughinis et al.· Computers· 0 citations
Design rules and a reproducible evaluation protocol are contributed that jointly report quality, memory, and end-to-end speed, and a foundation for automated pipeline search under realistic single-GPU constraints is provided.
Reported serving speedups from quantized kernels typically bundle the weight format, the kernel, and the inference runtime into one number. We present an attribution study on four NVIDIA RTX A5000 GPUs, 24 GiB each, on a single host with NVLink-bridged pairs. A matched intermediate stack that keeps the faster runtime without the quantized kernel splits the full speedup into a runtime part and a kernel and quantization part. Under matched greedy decoding the full stack reaches $2.58\times$ end to end, with the runtime change accounting for about two thirds of that gain on a logarithmic scale; across three similar model families the kernel and quantization part moves by at most 1.5%. Sharding one instance across all four cards falls well below doubling: a profiler trace attributes about 80% of the per token shortfall to coordination, and an NVLink versus PCIe control on the same hardware shows similar realized bandwidth on both links, pointing away from link bandwidth as the cause. Whether to run one sharded instance or several independent ones depends on the workload and the model, with the ranking reversing on the larger model: the smaller model splits between sharding and multiple instances by workload, while the larger model favors two paired instances on every workload. Quantization extends sustainable concurrent users roughly four times past a reproducible half precision memory cliff. Differences in sampling mode and prompt pool between the two stacks are documented as threats to validity.
Weijia Han, Lisha Qu· arXiv.org· 1 citation
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